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A6000 vs B300

Explore a head to head comparison of specifications, performance, and pricing.

A6000

The NVIDIA A6000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$2.42/hr
GPU VRAM48 GB
Cloud Availability6 clouds
System Memory512 GB
CPU Cores252
Storage2.6 TB

B300

The NVIDIA B300 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$36.20/hr
GPU VRAM288 GB
Cloud Availability2 clouds
System Memory3750 GB
CPU Cores240
Storage6.0 TB

A6000 vs B300: Which Should You Choose?

The B300 offers 288 GB of VRAM — 6× the 48 GB on the A6000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A6000 delivers 38.71 TFLOPS versus 1 TFLOPS on the B300 — 39× faster for mixed-precision training and inference. Memory bandwidth favors the A6000 at 0.77 TB/s compared to 0.01 TB/s on the B300, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A6000 is built on Ampere while the B300 uses Blackwell Ultra, reflecting different generational capabilities and optimizations. On Shadeform, the A6000 starts from $0.49/hr versus $7.40/hr for the B300 — 1410% more expensive — reflecting the performance premium. The A6000 is available across 6 cloud providers on Shadeform compared to 2 for the B300, giving more options for region and pricing flexibility.

A6000 — Best Use Cases

  • General-purpose deep learning training
  • Fine-tuning models up to 13B parameters
  • AI inference at moderate throughput
  • Computer vision and NLP workloads

Choose A6000 when:

  • 48 GB VRAM is sufficient for your workload
  • Cost efficiency is your primary concern
  • You are training large models or running high-throughput inference
  • You need flexibility across multiple cloud providers or regions

B300 — Best Use Cases

  • Next-generation LLM pre-training at scale
  • Trillion-parameter model inference
  • Ultra-high-throughput AI workloads
  • Advanced HPC and scientific computing

Choose B300 when:

  • You need 288 GB+ VRAM for large models or long context windows
  • Maximum performance justifies the higher cost
  • Your workload does not require peak FP16 throughput
  • Your preferred provider already has availability

See how the A6000 & B300 compare

Compare detailed hardware specifications and average pricing for the A6000 and B300.

Compare Hardware Specifications

A6000B300
GPU Type
A6000
B300
VRAM per GPU
48 GB
288 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Ampere
Blackwell Ultra
Interconnect
PCIe Gen4
SXM6
Memory Bandwidth
768 GB/s
8 TB/s
FP16 TFLOPS
38.71 TFLOPS (1:1)
1,231.8 TFLOPS (16:1)
CUDA Cores
10752
20480
Tensor Cores
336 (3rd Gen)
640 (5th Gen)
RT Cores
84 (2nd Gen)
N/A
Base Clock
1410 MHz
1665 MHz
Boost Clock
1800 MHz
2032 MHz
TDP
300W
1000W
Process Node
TSMC 8nm
TSMC 4NP
Data Formats
INT8, BF16, FP16, TF32, FP32
FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64

Compare Average On-Demand Pricing

A6000B300
1 GPU
$0.94 /hr
$7.40 /hr
2 GPUs
$1.89 /hr
$14.80 /hr
4 GPUs
$3.77 /hr
$29.20 /hr
8 GPUs
$4.16 /hr
$64.81 /hr

Frequently Asked Questions: A6000 vs B300

The main differences are VRAM (48 GB vs 288 GB), FP16 throughput (38.71 vs 1 TFLOPS), architecture (Ampere vs Blackwell Ultra). The A6000 uses the Ampere architecture while the B300 is based on Blackwell Ultra, giving each GPU different generational capabilities.

The A6000 is generally better for large language model training due to its higher throughput and 48 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.

On Shadeform, the A6000 is available from $0.49/hr. The B300 starts from $7.40/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.

The B300 has more VRAM at 288 GB, compared to 48 GB on the A6000. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.

Based on TFLOPS per dollar, the A6000 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the B300, paying the premium may be justified by faster job completion and lower total cost.

The A6000 is currently available across 6 cloud providers on Shadeform's network, compared to 2 for the B300. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.

Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either A6000 or B300. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore A6000 & B300 Instances

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